AEs have several tuning parameters. Each parameter affects the values in hidden layer, which is used to estimate dimension. Further tuning data hungry[1]} neural network is computationally intensive. We analyzed the sensitivity of dimension estimation algorithm by iteratively experimenting with range of lambda. Our goal was not find the optimal \(\lambda \) , but to study the effect of \(\lambda \)  on dimension of the dataset. Because financial markets are stochastic, dimension of markets not only changed due to \(\lambda \)  but also was amplified due to stress in market conditions. Overtuning neural network, especially in finance, can be detrimental. Having an hypothesis or a even broad range of financial market dimension[2]} mitigates the tricky exercise of training neural networks with limited real financial data.
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